Functional connectivity (FC) between regions of the brain can be assessed by the degree of temporal correlation measured with functional neuroimaging modalities. Based on the fact that these connectivities build a network, graph-based approaches for analyzing the brain connectome have provided insights into the functions of the human brain. The development of graph neural networks (GNNs) capable of learning representation from graph structured data has led to increased interest in learning the graph representation of the brain connectome. Although recent attempts to apply GNN to the FC network have shown promising results, there is still a common limitation that they usually do not incorporate the dynamic characteristics of the FC network which fluctuates over time. In addition, a few studies that have attempted to use dynamic FC as an input for the GNN reported a reduction in performance compared to static FC methods, and did not provide temporal explainability. Here, we propose STAGIN, a method for learning dynamic graph representation of the brain connectome with spatio-temporal attention. Specifically, a temporal sequence of brain graphs is input to the STAGIN to obtain the dynamic graph representation, while novel READOUT functions and the Transformer encoder provide spatial and temporal explainability with attention, respectively. Experiments on the HCP-Rest and the HCP-Task datasets demonstrate exceptional performance of our proposed method. Analysis of the spatio-temporal attention also provide concurrent interpretation with the neuroscientific knowledge, which further validates our method. Code is available at https://github.com/egyptdj/stagin

翻译：大脑各个区域之间的功能连接(FC)可以通过与功能神经成像模式测量的时间相关性程度来评估。基于这些连接建立网络的事实,分析大脑连接的图形化方法为人类大脑功能提供了洞察力。开发能够从图形结构化数据中学习表现的图形神经网络(GNNs)导致人们更加有兴趣学习大脑连接体的图示。虽然最近试图将GNN应用到功能神经成像网络的尝试已经显示出有希望的结果,但是仍然有一个共同的限制,即它们通常不包含FC网络的动态特征,这种动态特征会随时间波动。此外,一些试图将动态FC作为GNNE输入的图形化方法为GNNN提供了对人类大脑连接体的功能的洞察力。我们建议STAGIN是学习大脑连接体与空间-时间性关注的动态图形显示的一种方法。一个大脑图形的时序是输入STAGIN以获得动态图表显示的动态图形显示,而REDOUT功能和变压式数据分析方法也分别向我们的超常识性解释了HDLARC数据分析方法。